In this paper, a bottom-up hierarchical genetic algorithm is proposed to visualize clustered data into a planar graph. To achieve global optimization by accelerating local optimiz...
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
—Normalization before clustering is often needed for proximity indices, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attribute...
Background: Detecting groups of functionally related proteins from their amino acid sequence alone has been a long-standing challenge in computational genome research. Several clu...
Tobias Wittkop, Jan Baumbach, Francisco P. Lobo, S...
The immensevolumeof data resulting from DNAmicroarray experiments, accompaniedby an increase in the numberof publications discussing gene-related discoveries, presents a majordata...
Hagit Shatkay, Stephen Edwards, W. John Wilbur, Ma...